Apr 23, 2025 · To address this, we propose the Masked Diffusion Spatio-Temporal Autoencoder (MDSTA) network for the joint classification of remote sensing data under arbitrary modalities. …

Apr 6, 2023 · While directly pre-training with diffusion models does not produce strong representations, we condition diffusion models on masked input and formulate diffusion …

learning to regress pixels of masked patches given the other visible patches. Inspired by MAE, we incorporate masking into diffusion models and cast Diffusion Models as Masked Autoencoders …

Nov 25, 2024 · In this article, we explore spectral and spatial remote sensing image features as improved MAE-reconstruction targets. We first conduct a study on reconstructing various …

Pre-train a Masked Autoencoder with the idea of Diffusion Models for Hyperspectral Image Classification.

arious image features, all performing comparably well or better than raw pixels. Based on such observations, we propose Feature Guided Masked Autoencoder (FG-MAE): reconstructing a …

In this paper, we introduce Fus-MAE, a self-supervised learning framework based on masked autoencoders that uses cross-attention to perform early and feature-level data fusion between …

We introduce a novel pretraining framework called Saliency Supervised Masked Autoencoder (SSMAE), which improves the traditional masked autoencoder by incorporating saliency …

Feb 28, 2025 · Using a novel variation on the masked autoencoder (MAE) framework, our model incorporates a dual-task setup: reconstructing masked Sentinel-2 images and predicting …

Mar 24, 2024 · In addition, we incorporate masked training to the diffusion autoencoder to achieve local diffusion, which significantly reduces the training cost of model. The approach is tested …